A Fuzzy Logic Approach to Supervised Segmentation for Object- Oriented Classification

Object-oriented classification has shown a great potential in the classification of very high resolution satellite images, such as QuickBird and Ikonos. In the object-oriented classification, object segmentation is a crucial process and it significantly influences the classification results. Current techniques heavily rely on the operator’s experience to find appropriate segmentation parameters for achieving an acceptable classification result, which is a labour intensive and time consuming work. The success of the classification often depends on the knowledge and expertise of the operator. This paper presents a fuzzy logic approach to the determination of suitable object segmentation parameters leading to an improved object-oriented classification result. To obtain a set of optimum object segmentation parameters, an initial segmentation needs to be applied to the input image at a finer scale for identifying object’s primitive segments. The primitive segments are then used to train the fuzzy logic system. After the training, a set of optimum segmentation parameters can be found by the fuzzy logic system, resulting in optimum classification results. This fuzzy logic approach also significantly increases the classicisation efficiency by reducing iterative, knowledge-based, interactive adjustments of segmentation parameters. Testing rests demonstrated that this approach is promising to significantly improve current object-oriented classification techniques.